Results 121 to 130 of about 4,872 (167)

Fusion of Multispectral LiDAR and Hyperspectral Imagery

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents a technique for the fusion of multispectral LiDAR and hyperspectral data. The proposed method is based on the fusion of the features of multispectral LiDAR and hyperspectral data projected in two different subspaces. First, the spatial features are extracted from both data using morphological filters.
Rasti, B., Ghamisi, P., Gloaguen, R.
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Restoration of hyperspectral imagery

SPIE Proceedings, 2006
In hyperspectral imaging, the quality of the collected spectral signatures can be degraded by blurring due to the channel weighting function of the imaging spectrometer. In this work, we are investigating reconstruction techniques to enhance salient features and remove degradation effects in measured spectra to assist in subsequent machine analysis.
Alejandra Umaña-Díaz, Miguel Vélez-Reyes   +1 more
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Adaptive coding of hyperspectral imagery

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
Two systems are presented for compression of hyperspectral imagery. These systems utilize adaptive classification, trellis-coded quantization, and optimal rate allocation. In the first system, DPCM is used for spectral decorrelation, while an adaptive wavelet-based coding scheme is used for spatial decorrelation.
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Dynamic band selection for hyperspectral imagery

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper presents a new BS, called dynamic BS (DBS) which revolutionizes the commonly used BS by considering the number of bands to be selected, p as a variable which varies with criterion used for BS and different applications. Its idea is derived from information theory where it assumes that signal sources are considered as source alphabets with ...
Keng-Hao Liu, Chein-I Chang
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Fast Band Selection for Hyperspectral Imagery

2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011
Band selection is a common technique for dimensionality reduction of hyperspectral imagery. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. However, it may be time-consuming for unsupervised band selection methods that need to take all pixels into ...
He Yang, Qian Du 0001
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Lossless Compression of Hyperspectral Imagery

2011 First International Conference on Data Compression, Communications and Processing, 2011
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances ...
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Sparsity-based classification of hyperspectral imagery

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
In this paper, a new sparsity-based classification algorithm for hyperspectral imagery is proposed. This algorithm is based on the concept that a pixel in hyperspectral imagery lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples.
Yi Chen 0014, Nasser M. Nasrabadi, Trac D. Tran   +2 more
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Exploiting manifold geometry in hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2005
A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed and compared against the de facto standard of linear mixing. This new approach seeks a manifold coordinate system that preserves geodesic distances in the high-dimensional hyperspectral data space. Algorithms for deriving manifold coordinates, such as isometric
Charles M. Bachmann, Thomas L. Ainsworth, Robert A. Fusina   +2 more
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Robust Sparse Unmixing for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2018
A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library.
Dan Wang 0005, Zhenwei Shi 0001, Xinrui Cui   +2 more
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Hyperspectral Imagery Clustering With Neighborhood Constraints

IEEE Geoscience and Remote Sensing Letters, 2013
This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area.
Shanshan Li 0003, Bing Zhang 0001, An Li, Xiuping Jia, Lianru Gao, Man Peng   +5 more
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